Instance-Optimal Estimation with Multiple LLM Judges on a Budget
Evaluating large language models increasingly relies on LLM-as-a-judge protocols, but such evaluations remain costly: different judges have different prices and reliabilities, and …
Sanghwa Kim
A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
We consider the problem of heteroskedastic generalized linear bandits (GLBs) with adversarial corruptions, which subsumes various stochastic contextual bandit settings, including …
sanghwa-kim
Preliminary Empirical Study of Low-Rank, Hierarchical Gaussian Linear Bandits
Inspired by recent advances in multi-task bandits, we propose a new problem setting called low-rank, hierarchical Gaussian linear bandits, which combines low-rank structure with …